Learning and Cognitive Systems


Cognitive Modeling and Bayesian Identification Analysis (CoMBIAn), work package within project Impact of affective and informative feedback on learning in children before and after a reattribution training: An integrated approach using neuroimaging, educational research and modeling, Möbus, Moschner, Parchmann & Thiel (main applicant), BMBF-Programme for the Promotion of Scientific Collaboration between the Neurosciences and Research on Learning and Instruction, 1.3.2008 – 28.2.2011

This work package will use the behavioural data and the neural data obtained by fRMI experiments. Cognitive Models of the learner will be embedded in the cognitive state-of-the-art architecture ACT-R. The classic workflow to analyse behavioural and neural data with ACT-R is i) Task Analysis and Goal Hierarchy; ii) conceptualization of a cognitive model in ACT-R; iii) model trace with dynamic module activations and iv) prediction of BOLD-activities in selected brain regions and behavioural data. In order to predict behavioural and neural data, this workflow will be extended by two new aspects, the incorporation of an affective module to the architecture and the use of a Bayesian dynamic network to revise Anderson’s brain mapping hypothesis.